Special Issue "Artificial Intelligence and Ambient Intelligence"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence Circuits and Systems (AICAS)".

Deadline for manuscript submissions: closed (31 March 2021).

Special Issue Editors

Prof. Dr. Matjaz Gams
E-Mail Website
Guest Editor
Jozef Stefan Institute, Ljubljana, Slovenia
Interests: artificial intelligence; intelligent systems; ambient intelligence; information society; machine learning
Dr. Martin Gjoreski
E-Mail Website
Guest Editor
Jozef Stefan Institute, Ljubljana, Slovenia
Interests: affective computing; machine learning; deep learning; sensor; mobile computing; mobile health

Special Issue Information

Dear Colleagues,

Ambient intelligence (AmI) and artificial intelligence (AI) both rely on AI methods applied to computing devices. Furthermore, their power stems from the same advancement of electronics, sensors, and software development. Yet, AmI is not just an AI application serving humans, and by its definition it is even more aligned to interactions with humans. Be it smart home or autonomous car, it is essential for the AmI system to be familiar with the user’s desired performance as well as the current state of the mind. As such, the human-system relation is of a predominant importance, and it represents one of the most fruitful, but often neglected fields of research for AI.

Strategically, the hardware (HW) and software (SW) exponential development was essential not only for AI and AmI, but for the overall human civilization as well. Information society laws such as Moore’s Law or Keck’s Law describe the progress of electronic computing and data transfer and storage devices. While several limitations of specific HW characteristics (e.g., the speed of the processor chip) have already been met, it is not clear which are the next promising technology fields to enable further HW progress. Is it that we are facing a slow but steady decline following the fast exponential growth? Are there major possibilities of improvements by connecting SW, AI, and AmI methods directly to the chips? Should we integrate the flexibility of SW with the speed of electronic HW and vastly improve the cognitive and computing powers? Will AmI benefit more through this progress, since is it intrinsically devoted to connecting devices and humans? Which one will bring the most benefit to the human society and which one will first achieve superintelligence – general AI or general AmI?

Answers to these and related questions represent the core of this Special Issue. In order to cope with the abovementioned obstacles, original studies and either viewpoints, theoretical analyses or modeling methods can be developed and proposed to foster further progress.

Specific Topics

We invite researchers to contribute original research articles as well as review articles to present their proposals, views, and studies in the field of AmI and AI in relation to the overall HW, SW, and human civilization progress. Submissions can focus on the research concept or applied research in topics including, but not limited to, the following:

  • Applications in COVID-19: patient monitoring, patient rehabilitation, brain-computer interfaces,assisted living and caring, fall detection, elderly care,interventions for psychological crises
  • Applications in smart homes and smart buildings
  • Mobile/wearable intelligence
  • Robotics applied to smart environments
  • Applications of combined pervasive / ubiquitous computing with AI
  • Use of mobile, wireless, visual, and multi-modal sensor networks in intelligent systems
  • Intelligent handling of privacy, security and trust

Prof. Dr. Matjaz Gams
Dr. Martin Gjoreski
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Ambient intelligence
  • Artificial intelligence
  • Superintelligence
  • Multi-agent systems
  • Ambient assisted living
  • Computational intelligence methods
  • Pervasive Computing
  • Mobile computing
  • Ubiquitous computing
  • Self-adaptation, self-organisation, and self-supervised learning
  • Intelligent interfaces (user-friendly man–machine interface)
  • AI-assisted medical diagnosis
  • Mobile sensing applications
  • Autonomous and social robots
  • IoT and cyber-physical systems

Published Papers (9 papers)

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Editorial

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Editorial
Artificial Intelligence and Ambient Intelligence
Electronics 2021, 10(8), 941; https://doi.org/10.3390/electronics10080941 - 15 Apr 2021
Viewed by 341
Abstract
Artificial intelligence (AI) and its sister ambient intelligence (AmI) have in recent years become one of the main contributors to the progress of digital society and human civilization [...] Full article
(This article belongs to the Special Issue Artificial Intelligence and Ambient Intelligence)

Research

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Article
Ultra-Short Window Length and Feature Importance Analysis for Cognitive Load Detection from Wearable Sensors
Electronics 2021, 10(5), 613; https://doi.org/10.3390/electronics10050613 - 06 Mar 2021
Cited by 1 | Viewed by 479
Abstract
Human cognitive capabilities are under constant pressure in the modern information society. Cognitive load detection would be beneficial in several applications of human–computer interaction, including attention management and user interface adaptation. However, current research into accurate and real-time biosignal-based cognitive load detection lacks [...] Read more.
Human cognitive capabilities are under constant pressure in the modern information society. Cognitive load detection would be beneficial in several applications of human–computer interaction, including attention management and user interface adaptation. However, current research into accurate and real-time biosignal-based cognitive load detection lacks understanding of the optimal and minimal window length in data segmentation which would allow for more timely, continuous state detection. This study presents a comparative analysis of ultra-short (30 s or less) window lengths in cognitive load detection with a wearable device. Heart rate, heart rate variability, galvanic skin response, and skin temperature features are extracted at six different window lengths and used to train an Extreme Gradient Boosting classifier to detect between cognitive load and rest. A 25 s window showed the highest accury (67.6%), which is similar to earlier studies using the same dataset. Overall, model accuracy tended to decrease as the window length decreased, and lowest performance (60.0%) was observed with a 5 s window. The contribution of different physiological features to the classification performance and the most useful features that react in short windows are also discussed. The analysis provides a promising basis for future real-time applications with wearable sensors. Full article
(This article belongs to the Special Issue Artificial Intelligence and Ambient Intelligence)
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Article
A One-Dimensional Non-Intrusive and Privacy-Preserving Identification System for Households
Electronics 2021, 10(5), 559; https://doi.org/10.3390/electronics10050559 - 27 Feb 2021
Cited by 1 | Viewed by 371
Abstract
In many ambient-intelligence applications, including intelligent homes and cities, awareness of an inhabitant’s presence and identity is of great importance. Such an identification system should be non-intrusive and therefore seamless for the user, especially if our goal is ubiquitous and pervasive surveillance. However, [...] Read more.
In many ambient-intelligence applications, including intelligent homes and cities, awareness of an inhabitant’s presence and identity is of great importance. Such an identification system should be non-intrusive and therefore seamless for the user, especially if our goal is ubiquitous and pervasive surveillance. However, due to privacy concerns and regulatory restrictions, such a system should also strive to preserve the user’s privacy as much as possible. In this paper, a novel identification system is presented based on a network of laser sensors, each attached on top of the room entry. Its sensor modality, a one-dimensional depth sensor, was chosen with privacy in mind. Each sensor is mounted on the top of a doorway, facing towards the entrance, at an angle. This position allows acquiring the user’s body shape while the user is crossing the doorway, and the classification is performed by classical machine learning methods. The system is non-intrusive, non-intrusive and preserves privacy—it omits specific user-sensitive information such as activity, facial expression or clothing. No video or audio data are required. The feasibility of such a system was tested on a nearly 4000-person, publicly available database of anthropometric measurements to analyze the relationships among accuracy, measured data and number of residents, while the evaluation of the system was conducted in a real-world scenario on 18 subjects. The evaluation was performed on a closed dataset with a 10-fold cross validation and showed 98.4% accuracy for all subjects. The accuracy for groups of five subjects averaged 99.1%. These results indicate that a network of one-dimensional depth sensors is suitable for the identification task with purposes such as surveillance and intelligent ambience. Full article
(This article belongs to the Special Issue Artificial Intelligence and Ambient Intelligence)
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Article
Device-Free Crowd Counting Using Multi-Link Wi-Fi CSI Descriptors in Doppler Spectrum
Electronics 2021, 10(3), 315; https://doi.org/10.3390/electronics10030315 - 29 Jan 2021
Cited by 1 | Viewed by 599
Abstract
Measuring the quantity of people in a given space has many applications, ranging from marketing to safety. A family of novel approaches to measuring crowd size relies on inexpensive Wi-Fi equipment, taking advantage of the fact that Wi-Fi signals get distorted by people’s [...] Read more.
Measuring the quantity of people in a given space has many applications, ranging from marketing to safety. A family of novel approaches to measuring crowd size relies on inexpensive Wi-Fi equipment, taking advantage of the fact that Wi-Fi signals get distorted by people’s presence, so by identifying these distortion patterns, we can estimate the number of people in such a given space. In this work, we refine methods that leverage Channel State Information (CSI), which is used to train a classifier that estimates the number of people placed between a Wi-Fi transmitter and a receiver, and we show that the available multi-link information allows us to achieve substantially better results than state-of-the-art single link or averaging approaches, that is, those that take the average of the information of all channels instead of taking them individually. We show experimentally how the addition of each of the multiple links information helps to improve the accuracy of the prediction from 44% with one link to 99% with 6 links. Full article
(This article belongs to the Special Issue Artificial Intelligence and Ambient Intelligence)
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Article
Constructing Emotional Machines: A Case of a Smartphone-Based Emotion System
Electronics 2021, 10(3), 306; https://doi.org/10.3390/electronics10030306 - 27 Jan 2021
Cited by 1 | Viewed by 560
Abstract
In this study, an emotion system was developed and installed on smartphones to enable them to exhibit emotions. The objective of this study was to explore factors that developers should focus on when developing emotional machines. This study also examined user attitudes and [...] Read more.
In this study, an emotion system was developed and installed on smartphones to enable them to exhibit emotions. The objective of this study was to explore factors that developers should focus on when developing emotional machines. This study also examined user attitudes and emotions toward emotional messages sent by machines and the effects of emotion systems on user behavior. According to the results of this study, the degree of attention paid to emotional messages determines the quality of the emotion system, and an emotion system triggers certain behaviors in users. This study recruited 124 individuals with more than one year of smartphone use experience. The experiment lasted for two weeks, during which time participants were allowed to operate the system freely and interact with the system agent. The majority of the participants took interest in emotional messages, were influenced by emotional messages and were convinced that the developed system enabled their smartphone to exhibit emotions. The smartphones generated 11,264 crucial notifications in total, among which 76% were viewed by the participants and 68.1% enabled the participants to resolve unfavorable smartphone conditions in a timely manner and allowed the system agent to provide users with positive emotional feedback. Full article
(This article belongs to the Special Issue Artificial Intelligence and Ambient Intelligence)
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Article
Gaining a Sense of Touch Object Stiffness Estimation Using a Soft Gripper and Neural Networks
Electronics 2021, 10(1), 96; https://doi.org/10.3390/electronics10010096 - 05 Jan 2021
Cited by 1 | Viewed by 752
Abstract
Soft grippers are gaining significant attention in the manipulation of elastic objects, where it is required to handle soft and unstructured objects, which are vulnerable to deformations. The crucial problem is to estimate the physical parameters of a squeezed object to adjust the [...] Read more.
Soft grippers are gaining significant attention in the manipulation of elastic objects, where it is required to handle soft and unstructured objects, which are vulnerable to deformations. The crucial problem is to estimate the physical parameters of a squeezed object to adjust the manipulation procedure, which poses a significant challenge. The research on physical parameters estimation using deep learning algorithms on measurements from direct interaction with objects using robotic grippers is scarce. In our work, we proposed a trainable system which performs the regression of an object stiffness coefficient from the signals registered during the interaction of the gripper with the object. First, using the physics simulation environment, we performed extensive experiments to validate our approach. Afterwards, we prepared a system that works in a real-world scenario with real data. Our learned system can reliably estimate the stiffness of an object, using the Yale OpenHand soft gripper, based on readings from Inertial Measurement Units (IMUs) attached to the fingers of the gripper. Additionally, during the experiments, we prepared three datasets of IMU readings gathered while squeezing the objects—two created in the simulation environment and one composed of real data. The dataset is the contribution to the community providing the way for developing and validating new approaches in the growing field of soft manipulation. Full article
(This article belongs to the Special Issue Artificial Intelligence and Ambient Intelligence)
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Article
On Robustness of Multi-Modal Fusion—Robotics Perspective
Electronics 2020, 9(7), 1152; https://doi.org/10.3390/electronics9071152 - 16 Jul 2020
Cited by 2 | Viewed by 931
Abstract
The efficient multi-modal fusion of data streams from different sensors is a crucial ability that a robotic perception system should exhibit to ensure robustness against disturbances. However, as the volume and dimensionality of sensory-feedback increase it might be difficult to manually design a [...] Read more.
The efficient multi-modal fusion of data streams from different sensors is a crucial ability that a robotic perception system should exhibit to ensure robustness against disturbances. However, as the volume and dimensionality of sensory-feedback increase it might be difficult to manually design a multimodal-data fusion system that can handle heterogeneous data. Nowadays, multi-modal machine learning is an emerging field with research focused mainly on analyzing vision and audio information. Although, from the robotics perspective, haptic sensations experienced from interaction with an environment are essential to successfully execute useful tasks. In our work, we compared four learning-based multi-modal fusion methods on three publicly available datasets containing haptic signals, images, and robots’ poses. During tests, we considered three tasks involving such data, namely grasp outcome classification, texture recognition, and—most challenging—multi-label classification of haptic adjectives based on haptic and visual data. Conducted experiments were focused not only on the verification of the performance of each method but mainly on their robustness against data degradation. We focused on this aspect of multi-modal fusion, as it was rarely considered in the research papers, and such degradation of sensory feedback might occur during robot interaction with its environment. Additionally, we verified the usefulness of data augmentation to increase the robustness of the aforementioned data fusion methods. Full article
(This article belongs to the Special Issue Artificial Intelligence and Ambient Intelligence)
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Article
PUT-Hand—Hybrid Industrial and Biomimetic Gripper for Elastic Object Manipulation
Electronics 2020, 9(7), 1147; https://doi.org/10.3390/electronics9071147 - 16 Jul 2020
Cited by 3 | Viewed by 835
Abstract
In this article, the design of a five-fingered anthropomorphic gripper is presented specifically designed for the manipulation of elastic objects. The manipulator features a hybrid design, being equipped with three fully actuated fingers for precise manipulation, and two underactuated, tendon-driven digits for secure [...] Read more.
In this article, the design of a five-fingered anthropomorphic gripper is presented specifically designed for the manipulation of elastic objects. The manipulator features a hybrid design, being equipped with three fully actuated fingers for precise manipulation, and two underactuated, tendon-driven digits for secure power grasping. For ease of reproducibility, the design uses as many off-the-shelf and 3D-printed components as possible. The on-board controller circuit and firmware are also presented. The design includes resistive position and angle sensors in each joint, resulting in full joint observability. The controller has a position-based controller integrated, along with USB communication protocol, enabling gripper state reporting and direct motor control from a PC. A high-level driver operating as a Robot Operating System node is also provided. All drives and circuitry of the PUT-Hand are integrated within the hand itself. The sensory system of the hand includes tri-axial optical force sensors placed on fully actuated fingers’ fingertips for reaction force measurement. A set of experiments is provided to present the motion and perception capabilities of the gripper. All design files and source codes are available online under CC BY-NC 4.0 and MIT licenses. Full article
(This article belongs to the Special Issue Artificial Intelligence and Ambient Intelligence)
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Review

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Review
Relations between Electronics, Artificial Intelligence and Information Society through Information Society Rules
Electronics 2021, 10(4), 514; https://doi.org/10.3390/electronics10040514 - 22 Feb 2021
Cited by 2 | Viewed by 499
Abstract
This paper presents relations between information society (IS), electronics and artificial intelligence (AI) mainly through twenty-four IS laws. The laws not only make up a novel collection, currently non-existing in the literature, but they also highlight the core boosting mechanism for the progress [...] Read more.
This paper presents relations between information society (IS), electronics and artificial intelligence (AI) mainly through twenty-four IS laws. The laws not only make up a novel collection, currently non-existing in the literature, but they also highlight the core boosting mechanism for the progress of what is called the information society and AI. The laws mainly describe the exponential growth in a particular field, be it the processing, storage or transmission capabilities of electronic devices. Other rules describe the relations to production prices and human interaction. Overall, the IS laws illustrate the most recent and most vibrant part of human history based on the unprecedented growth of device capabilities spurred by human innovation and ingenuity. Although there are signs of stalling, at the same time there are still many ways to prolong the fascinating progress of electronics that stimulates the field of artificial intelligence. There are constant leaps in new areas, such as the perception of real-world signals, where AI is already occasionally exceeding human capabilities and will do so even more in the future. In some areas where AI is presumed to be incapable of performing even at a modest level, such as the production of art or programming software, AI is making progress that can sometimes reflect true human skills. Maybe it is time for AI to boost the progress of electronics in return. Full article
(This article belongs to the Special Issue Artificial Intelligence and Ambient Intelligence)
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